The article studies the effect of polynorbornene (PNB) in the composition of PNB with Norman 747 LV plasticizer (RC) on the curing characteristics of the rubber compound and the physico-mechanical, dynamic, dielectric properties and the thermal behavior of vulcanizates based on a combination of isoprene, α-methylstyrene-butadiene, and nitrile-butadiene rubbers. It is shown that vulcanizates containing PNB in the composition of the RC had lower conditional tensile strength, hardness, and tear resistance compared to the vulcanizate of the base version of the rubber compound. Studies of dynamic mechanical analysis indicate that an increase in the content of RC, and hence PNB, in the rubber compound contributes to an increase in the mechanical loss factor (tanδ) and a decrease in the storage modulus of vulcanizates. It was found that vulcanized rubber, containing 24.0 parts per hundred of rubber (phr) (8.98 wt. %) PNB as part of the RC, is characterized by stable physico-mechanical, improved vibration-absorbing properties, as well as increased dielectric parameters. This rubber compound can be used as a base for rail fasteners for railroad tracks.

The influence of rubbers, trans-polynorbornene, polyisobutylene P-200 and sevilene on vulcanization characteristics, physical-mechanical and dynamic properties of rubber used for the manufacture of products in contact with sea water has been investigated. It has been established that the developed rubber with a sulfur vulcanizing system based on rubbers SKMS-30ARK, SKN-4065 and BK-1675 with additives of trans-polynorbornene and sevilene 11808-340 has improved physical and mechanical characteristics, resistance to sea water and increased dynamic properties

We study the problems of serviceability of contemporary high-precision terminals of global navigation satellite systems under the conditions of jamming and spoofing interferences. The application of digital antenna arrays with algorithms of space-time signal processing of the signals can be regarded as a solution of the problem of low interference resistance. We describe well-known, most studied, and currently applied algorithms of space-time signal processing. We also formulate the causes that do not enable one to use well-known algorithms in high-precision terminals of satellite systems of global navigation. We propose an algorithm of space-time signal processing based on a space-time filter of finite length with a theoretically justified requirement of Hermitian symmetry for the matrix impulse response. The proposed algorithm guarantees the absence of distortions of signals under any signal and interference conditions. In this case, the impulse response of the space-time filter is computed according to the criterion of optimal suppression of the interference. The characteristics of the proposed space-time filter and other algorithms of space-time signal processing are investigated. For this purpose, we apply the method of mathematical simulation with random search of numerous parameters of signals and interferences (directions to signals, directions to numerous interferences, and to their reflections, remoteness of the reflectors of interferences, the phases of reflections, and the levels of interferences and reflections, etc.). The results of simulations are presented in the form of the distribution functions of the signal-to-interference ratios at the output of the algorithms of space-time signal processing and the distribution functions of the phase and signal-time biases. The obtained dependences substantiate the absence of the phase and signal-time biases in the space-time filter for any signal and interference conditions with interference multipath. It is shown that the space-time filter guarantees a higher interference resistance than the compensating algorithms of space-time signal processing.

One of the most effective ways to improve the accuracy and speed of algorithms for searching and recognizing objects in images is to pre-select areas of interest in which it is likely to detect objects of interest. To determine areas of interest in a pre-processed radar or satellite image of the underlying surface, the Kohonen network was used. The found areas of interest are sent to the convolutional neural network, which provides the final detection and recognition of objects. The combination of the above methods allows to speed up the process of searching and recognizing objects in images, which is becoming more expedient due to the constantly growing volume of data for analysis. The process of preliminary processing of input data is described, the process of searching and recognizing patterns of aircraft against the underlying surface is presented, and the analysis of the results is carried out. The use of the Kohonen neural network makes it possible to reduce the amount of data analyzed by the convolutional network by 18–125 times, which accordingly accelerates the process of detection and recognition of the object of interest. The size of the parts of the input image fed into the convolutional network, into which the zones of interest are divided, is tied to the image scale and is equal to the size of the largest detectable object, which can significantly reduce the training sample. Application of the presented methods and centering of the object on training images allows accelerating the convolutional network training by more than 5 times and increasing the recognition accuracy by at least 10%, as well as minimizing the required minimum number of layers and neurons of the network by at least halving, respectively increasing its speed

Today, there is a serious need to improve the performance of algorithms for detecting objects in images. This process can be accelerated with the help of preliminary processing, having found areas of interest on the images where the probability of object detection is high. To this end, it is proposed to use the algorithm for distinguishing the boundaries of objects using the Sobel operator and Kohonen self-organizing maps, described in this paper and shown by the example of determining zones of interest when searching and recognizing objects in satellite images. The presented algorithm allows 15–100 times reduction in the amount of data arriving at the convolutional neural network, which provides the final recognition. Also, the algorithm can significantly reduce the number of training images, since the size of the parts of the input image supplied to the convolution network is tied to the image scale and equal to the size of the largest recognizable object, and the object is centered in the frame. This allows to accelerate network learning by more than 5 times and increase recognition accuracy by at least 10 %, as well as halve the required minimum number of layers and neurons of the convolutional network, thereby increasing its speed.

Today, the use of artificial intelligence based on neural networks is the most effective approach to solving image recognition problems. The possibility of using a convolutional neural network to create a pattern detector for technical analysis based on stock chart data has been investigated. The found figures of technical analysis can serve as the basis for making trading decisions in the financial markets. In the conditions of an ever-growing array of various information, the use of visual data reading tools is becoming more and more expedient, as it allows to speed up the process of searching and processing the necessary information for decision-makers. The modeling process, analysis, and results of applying the pattern detector of technical analysis are presented. The general approach to the construction and learning of a convolutional neural network is also described, and the process of preliminary processing of input data is described. Using the created detector allows to automate the search for patterns and improve the accuracy of making trading decisions. After finding the patterns, it becomes possible to obtain additional stock statistics for each type of figure: the context in front of the figures, the percentage of successfully completed figures, volume analysis, etc. These technical solutions can be used as expert and trading systems in the stock market, as well as integrated into existing ones.

This paper sets out the properties and the results of the testing of new antiozonants 2PPD and 3PPD in place of standard and imported 6PPD and IPPD which are used in the production of mechanical rubber goods and tyres (in the sidewall, tread, and other elements). The proposed method for producing the new antiozonants makes it possible to organise their industrial production by low-cost technology, in contrast to 6PPD and IPPD production technology. The raw materials for the new antiozonants include ethylene and propylene glycols, available in Russia, and 4-aminodiphenylamine, imported for the production in Russia of antioxidant S789, which is widely used in the production of large-tonnage rubbers, in particular synthetic isoprene rubber SKI-3, over 70% of the volume of which is exported. A number of advantages of 3PPD over imported stabilisers have been established. These include a greater effectiveness in protecting rubber compounds against thermal oxidation and ozone cracking by comparison with the stabilisers it is replacing. The shortcomings of the new antiozonant (in terms of the form in which it is produced and in terms of its influence on the vulcanisation kinetics) are considered, and various ways to overcome these shortcomings are presented.